Uncertainty evaluation via PAvPU: With the \(p\) -value of the testing result and a given \(p\) -value threshold, we can determine whether the model is certain or uncertain about one prediction. To evaluate the uncertainty estimates, we uses Patch Accuracy vs Patch Uncertainty (PAvPU) [1]}, which is defined as \(\mathrm {PAvPU}={\left(n_{a c}+n_{i u}\right)}/{\left(n_{a c}+n_{a u}+n_{i c}+n_{i u}\right)}\) ,
where \(n_{ac}, n_{au}, n_{ic}, n_{iu}\)  are the numbers of accurate and certain, accurate and uncertain, inaccurate and certain, inaccurate and uncertain samples, respectively. This PAvPU evaluation metric would be higher if the model tends to generate the accurate prediction with high certainty and inaccurate prediction with high uncertainty.
